I'm a bit lost at the results part of my thesis. I have conducted an generalized maximum likelihood (GML) univariate test with Y variable: dependent variable one X variable: independent (consists of 2 conditions)
I have a lot of covariates to control for. So i performed different ANOVA's with just one covariate at a time.
Some covariates turn out to be significant AND the X variable is also significant.
Some covariates turn out to be significant but the X variable is not significant.
I previously wrote in the results section of my thesis, that when a covariate turns out to be significant and the X was less significant than in the analysis without the covariates, this means I found a main effect but it did not change the interpretation of the results.
But what is the case if the some covariates are not significant, but the x variable is significant and even more significant than in the analysis without covariates?
What can I conclude from this finding? I tried to google it but i couldn't find it. I hope you can help me.
A lot of thanks in advance.